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<h4>Setup ICA Defaults</h4>
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<p>The explanation of hidden user interface controls is as follows:</p>
<dl>	

<dt> Select Type Of PCA</dt>
<dd> There are two options like 'Standard' and 'Expectation Maximization'. 
Optional parameters for PCA are provided. Please see <a href="../icatb_pca_options.htm">
PCA options</a> 
page. </dd>

<dt> Select The Back-Reconstruction Type</dt>
	<dd>Options available are GICA, Regular (GICA2), GICA3 and Spatial-temporal 
	regression. GICA2 and GICA3 are not not shown in the GUI but can be called 
	in the batch script. GICA is a more robust tool to back reconstruct 
	components when compared to GICA2 and GICA3 for low model order.</dd>
	<ul>
	<li> GICA - GICA uses PCA whitening and dewhitening information to back 
	reconstruct the component topographies and timecourses (<a href="../gift/icatb_references.htm">[1]</a>).</li>
<li> Spatial-temporal Regression - Back reconstruction is done using a two step 
multiple regression (<a href="../gift/icatb_references.htm">[3]</a>). In the 
first step, aggregate component timecourses are used as basis functions and projected on to 
the subject's data resulting in subject component topographies. In the second 
step, subject component topographies are used as basis functions and projected 
on to the subject's data resulting in component timecourses for that subject. </li>

</ul>

<dd>
<strong>Note:</strong>
<ul>
	<li>GICA, GICA2 topographies are similar to the topographies obtained using 
	Spatial-temporal Regression.</li>
	<li>Timecourses obtained using GICA2 are exactly equal to the GICA3 method.</li>
	<li>All the back reconstruction methods give the same timecourses and 
	topographies for one single subject single session analysis.</li>
	<li>GICA and Spatial-temporal Regression component topographies are 
	equivalent when 100% variance is retained in the first step PCA.</li>
</ul>
	</dd>


	<dt> Do You Want To Scale Components? </dt>
	<dd>Scale To Original Data	
	<ul>
		<li> Components will be scaled to original data units by doing a 
		multiple regression using the time domain average of the component as 
		model and the average of the EEG signal from the original data as 
		observation. For each component, EEG signal from the original data is 
		computed by selecting the electrode that has the maximum absolute value. </li>
	</ul>
	</dd>
	<dd>Z-scores<ul>
		<li>Component images and time courses are divided by their standard 
		deviation.</li>
	</ul>
	</dd>
		<dd>Scaling in Topographies<ul>
		<li>Timecourses are normalized using the maximum amplitude and the 
		maximum amplitude of timecourses is multiplied to the topographies.</li>
	</ul>
	</dd>

	<dd>Scaling in Timecourses and Topographies<ul>
		<li>Timecourses are scaled using the standard deviation of topographies 
		and topographies are scaled using the maximum amplitude of timecourses.</li>
	</ul>
	</dd>

	<dt>How Many Data Reduction (PCA) Steps Do You Want to Run? </dt>
	<dd>The number of times you want to do PCA. </dd>
	<dd><b>Note</b>: The number of data reduction steps depends on the number of 
	data sets. A maximum of three data reduction steps is allowed. </dd>
	<dt>Number of PC to reduce each group into </dt>
	<dd>Each subject is reduced to the number of principal components selected.
	<ul>
		<li>Number of times the prompt strings requesting to enter the PC is 
		equal to the number of data reduction steps. </li>
	</ul>
	</dd>
	<dd><b>Note</b>:
	<ol>
		<li>Always choose a greater number for PC in the first step if you wish 
		to choose different numbers for the principal components. </li>
		<li>The number of principle components after the last step of data 
		reduction (will be disabled as you already entered the number of 
		independent components) is the same as the number of independent 
		components you want to extract.
		</li>
	</ol>
	</dd>
</dl>

<p class="style1"> Figure 1: Setup Defaults menu </p>
<p class="style1"> 
<img border="0" src="images/icatb_eeg_setup_ica_defaults.jpg"> </p>

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